{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646ee017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read  /tmp/shares/record/2023-06-30/flow/stock/002031.csv\n",
      "read  /tmp/shares/record/2023-06-30/real/stock/002031.csv\n",
      "     序号    代码    名称   最新价  今日涨跌幅  今日主力净流入-净额 今日主力净流入-净占比  今日超大单净流入-净额   \n",
      "0     7  2031  巨轮智能  4.48  10.07    0.999553       12.29     0.999505  \\\n",
      "48    7  2031  巨轮智能  4.48  10.07    0.999597       12.29     0.999553   \n",
      "119   7  2031  巨轮智能  4.48  10.07    0.999613       12.29     0.999570   \n",
      "120   7  2031  巨轮智能  4.48  10.07    0.999724       12.29     0.999694   \n",
      "127   7  2031  巨轮智能  4.48  10.07    1.000000       12.29     1.000000   \n",
      "\n",
      "    今日超大单净流入-净占比  今日大单净流入-净额 今日大单净流入-净占比    今日中单净流入-净额 今日中单净流入-净占比   \n",
      "0          11.08  19411440.0        1.21  -111092000.0       -6.95  \\\n",
      "48         11.08  19411440.0        1.21  -111092000.0       -6.95   \n",
      "119        11.08  19411440.0        1.21  -111092000.0       -6.95   \n",
      "120        11.08  19411440.0        1.21  -111092000.0       -6.95   \n",
      "127        11.08  19411440.0        1.21  -111092000.0       -6.95   \n",
      "\n",
      "      今日小单净流入-净额 今日小单净流入-净占比    time            datetime  \n",
      "0    -85466112.0       -5.34  130453 2023-06-30 13:04:53  \n",
      "48   -85474624.0       -5.34  130503 2023-06-30 13:05:03  \n",
      "119  -85477760.0       -5.34  130513 2023-06-30 13:05:13  \n",
      "120  -85499712.0       -5.35  130524 2023-06-30 13:05:24  \n",
      "127  -85553920.0       -5.35  130535 2023-06-30 13:05:35  \n",
      "    序号    代码    名称  最新价    涨跌幅   涨跌额       成交量           成交额    振幅    最高  ...   \n",
      "69  24  2031  巨轮智能  1.0  10.07  0.41  0.999983  1.599378e+09  4.67  4.48  ...  \\\n",
      "40  24  2031  巨轮智能  1.0  10.07  0.41  0.999985  1.599380e+09  4.67  4.48  ...   \n",
      "21  23  2031  巨轮智能  1.0  10.07  0.41  0.999986  1.599382e+09  4.67  4.48  ...   \n",
      "2   23  2031  巨轮智能  1.0  10.07  0.41  1.000000  1.599404e+09  4.67  4.48  ...   \n",
      "61  23  2031  巨轮智能  1.0  10.07  0.41  1.000000  1.599404e+09  4.67  4.48  ...   \n",
      "\n",
      "         换手率  市盈率-动态  市净率           总市值          流通市值   涨速  5分钟涨跌  60日涨跌幅   \n",
      "69  0.999464  446.18  4.0  9.853293e+09  8.663486e+09  0.0    0.0   38.27  \\\n",
      "40  0.999464  446.18  4.0  9.853293e+09  8.663486e+09  0.0    0.0   38.27   \n",
      "21  0.999464  446.18  4.0  9.853293e+09  8.663486e+09  0.0    0.0   38.27   \n",
      "2   1.000000  446.18  4.0  9.853293e+09  8.663486e+09  0.0    0.0   38.27   \n",
      "61  1.000000  446.18  4.0  9.853293e+09  8.663486e+09  0.0    0.0   38.27   \n",
      "\n",
      "    年初至今涨跌幅    time  \n",
      "69    44.05  130454  \n",
      "40    44.05  130502  \n",
      "21    44.05  130511  \n",
      "2     44.05  130521  \n",
      "61    44.05  130529  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import time\n",
    "import os\n",
    "import datetime\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as py\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "date = '2023-06-30'\n",
    "code = '002031'\n",
    "record_root = '/tmp/shares/record'\n",
    "\n",
    "flow_record = '%s/%s/flow/stock/%s.csv' % (record_root, date, code)\n",
    "real_record = '%s/%s/real/stock/%s.csv' % (record_root, date, code)\n",
    "\n",
    "print('read ', flow_record)\n",
    "print('read ', real_record)\n",
    "\n",
    "def draw():\n",
    "    if (not os.path.exists(flow_record)) or (not os.path.exists(real_record)):\n",
    "        print(\"data not full\")\n",
    "        return\n",
    "\n",
    "    ss = MaxAbsScaler()\n",
    "\n",
    "    flow_df = pd.read_csv(flow_record)\n",
    "    flow_df = flow_df.loc[flow_df['今日主力净流入-净额'] != '-']\n",
    "    flow_df = flow_df.sort_values(by='time', ascending=True, inplace=False)\n",
    "    scale_features = ['今日超大单净流入-净额', '今日主力净流入-净额']\n",
    "    flow_df[scale_features] = ss.fit_transform(flow_df[scale_features])\n",
    "\n",
    "    for index, row in flow_df.iterrows():\n",
    "        flow_df.loc[index, 'datetime'] = datetime.datetime.strptime('%s %s' % (date, row['time']), \"%Y-%m-%d %H%M%S\")\n",
    "\n",
    "    real_df = pd.read_csv(real_record)\n",
    "    real_df = real_df.sort_values(by='time', ascending=True, inplace=False)\n",
    "    scale_features = ['最新价', '换手率', '成交量', '5分钟涨跌']\n",
    "    real_df[scale_features] = ss.fit_transform(real_df[scale_features])\n",
    "    \n",
    "    print(flow_df.tail(5))\n",
    "    print(real_df.tail(5))\n",
    "\n",
    "    for index, row in flow_df.iterrows():\n",
    "        flow_df.loc[index, 'datetime'] = datetime.datetime.strptime('%s %s' % (date, row['time']), \"%Y-%m-%d %H%M%S\")\n",
    "\n",
    "\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=flow_df.datetime,\n",
    "                         y=flow_df['今日超大单净流入-净额'],\n",
    "                         mode = 'lines',\n",
    "                         name = '今日超大单净流入-净额'))\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=flow_df.datetime,\n",
    "                         y=flow_df['今日主力净流入-净额'],\n",
    "                         mode = 'lines',\n",
    "                         name = '今日主力净流入-净额'))\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=flow_df.datetime,\n",
    "                         y=real_df['最新价'],\n",
    "                         mode = 'lines',\n",
    "                         name = '最新价'))\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=flow_df.datetime,\n",
    "                         y=real_df['换手率'],\n",
    "                         mode = 'lines+markers',\n",
    "                         name = '换手率'))\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=flow_df.datetime,\n",
    "                         y=real_df['成交量'],\n",
    "                         mode = 'lines',\n",
    "                         name = '成交量'))\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=flow_df.datetime,\n",
    "                         y=real_df['5分钟涨跌'],\n",
    "                         mode = 'lines',\n",
    "                         name = '5分钟涨跌'))\n",
    "\n",
    "    fig.show()\n",
    "\n",
    "draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96c626c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting scikit-learn\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/f0/dc/898e462497fcbaf8ab2e744c58acf463f74e542f71956fc364bf156c262c/scikit_learn-1.3.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.5 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.5/10.5 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m0m\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from scikit-learn) (1.22.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /home/ubuntu/.local/lib/python3.8/site-packages (from scikit-learn) (1.8.0)\n",
      "Collecting joblib>=1.1.1 (from scikit-learn)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/28/08/9dcdaa5aac4634e4c23af26d92121f7ce445c630efa0d3037881ae2407fb/joblib-1.3.1-py3-none-any.whl (301 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting threadpoolctl>=2.0.0 (from scikit-learn)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
      "Successfully installed joblib-1.3.1 scikit-learn-1.3.0 threadpoolctl-3.1.0\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting plotly\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a5/07/5bef9376c975ce23306d9217ab69ca94c07f2a3c90b17c03e3ae4db87170/plotly-5.15.0-py2.py3-none-any.whl (15.5 MB)\n",
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      "\u001b[?25hCollecting tenacity>=6.2.0 (from plotly)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e7/b0/c23bd61e1b32c9b96fbca996c87784e196a812da8d621d8d04851f6c8181/tenacity-8.2.2-py3-none-any.whl (24 kB)\n",
      "Requirement already satisfied: packaging in /home/ubuntu/.local/lib/python3.8/site-packages (from plotly) (23.1)\n",
      "Installing collected packages: tenacity, plotly\n",
      "Successfully installed plotly-5.15.0 tenacity-8.2.2\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n",
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48ff5aab",
   "metadata": {},
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       "                            \n",
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       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "# Create random data with numpy\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "\n",
    "N = 100\n",
    "random_x = np.linspace(0, 1, N)\n",
    "random_y0 = np.random.randn(N) + 5\n",
    "random_y1 = np.random.randn(N)\n",
    "random_y2 = np.random.randn(N) - 5\n",
    "\n",
    "\n",
    "fig = go.Figure()\n",
    "# Add traces\n",
    "fig = px.scatter(x=random_x, y=random_y0)\n",
    "fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines+markers', name='lines+markers'))\n",
    "fig.add_trace(go.Scatter(x=random_x, y=random_y2, mode='lines', name='lines'))\n",
    "fig.add_trace(go.Histogram(x=random_y2,y=random_x,marker = dict(color='rgba(255,0,0,0.6)')))\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e2c6806",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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